3.1 Target Group

Students who have a background in STEM who want to specialise in Computer Science whilst obtaining a holistic background on the applications of Computer Science and its fields. Students need to have completed a BSc in a STEM subject and a C1 level of English.

3.2 Entry Requirements

Students who have no training in the field must have completed a bachelor's in Computer Science, Information Technology or in a STEM subject. This applies to students applying for the MSc programme, the Post-graduate certificate, the Post-graduate diploma and the awards.

Students without the required background may be allowed to join the course depending on the students' circumstances and background (2 to 5 years of industry experience may also be considered).

A good grasp of scientific English is also required in order to follow the course. Students will be asked to provide an IELTS certificate higher than grade 7 (or equivalent) or proof of an equivalent level of English before commencing the course if the student has not followed their BSc in a primarily English-speaking country.

Candidates will be asked to present their previously obtained qualifications along with their respective transcripts.

To be promoted to the second semester, in which students will be specialising in a particular topic, students must have passed all the first semester credits.

MSc in Computer Science (FinTech and Blockchain)

DLT Developer, Software Engineer, Trading Analyst, Machine Learning Engineer, Team Lead

5.1 Knowledge and Understanding

• Advanced understanding of computer science theory and principles: Graduates of the program should have a deep understanding of the fundamental concepts and principles that underpin computer science, including algorithms, data structures, complexity theory, and programming languages.
• Ability to design, implement and evaluate complex computer systems: Graduates should be able to design, implement and evaluate large and complex computer systems, using a range of programming languages, software development methodologies, and tools.
• Ability to conduct research and contribute to the advancement of computer science: Graduates should have the ability to conduct original research in computer science, and to contribute to the advancement of the field through publications, presentations, and other forms of scholarly communication.
• Ability to apply computer science to real-world problems: Graduates should be able to apply their knowledge of computer science to solve real-world problems, and to work effectively in interdisciplinary teams.

5.2 Skills

• Strong problem solving, analytical and critical thinking skills: Graduates should have strong problem-solving, analytical and critical thinking skills which they should be able to apply to complex computer science problems.
• Proficiency in the use of current technologies and tools: Graduates should have a good proficiency with current technologies and tools, including programming languages, development environments, and software engineering tools.
• Communication and leadership skills. Graduates should have strong communication and leadership skills, enabling them to effectively explain complex technical ideas to non-technical audiences and to manage teams of developers.
• Awareness and understanding of ethical, social and professional issues in computing. Graduates should be aware of ethical, social and professional issues in computing and be able to navigate them and make informed decisions in their professional practice.

8.1 General Pedagogical Methods

• When it comes to pedagogical methods, the programme includes a wide variety of student-centric methods to cater for different learning styles. All components of the programmes will be delivered face-to-face. The modes of delivery include lectures, tutorials, group work, project work, hackathons and competitions.
• In classes with students, lecturers draw from academic and professional literature, present real-world case studies, present current affairs, show videos such as TED talks, invite experts on the topic as guest speakers, and ask students to actively participate in their own learning. Students are also asked to prepare questions and comments on current affairs or on an agreed article to be discussed during tutorials.
• For students who require additional assistance, lecturers can also be reached by email to support students in their learning process. Facilities include classrooms equipped with presentation tools, computer labs with workstations, virtual machine images, high-speed internet connectivity, virtual learning environments, and cloud computing resources.

8.2 General Assessment Methods

Assessment methods include written examinations, coursework assignments, capstone projects, presentations, case studies, project work, and class participation. Most modules have a substantial assignment component which may vary from term papers to implementing algorithms stemming from the unit. The specific assessment weighting is detailed in each module descriptor.

8.3 Grading and Progression

For each module, students are required to achieve the minimum pass mark. Students who fail the module have an opportunity to resit. Should the student fail a second time, they will need to repeat the complete module.

Depending on whether the student chooses to complete the programme on a part-time or full-time basis, this will be included in the title of the final certificate.

4. Master of Science (MSc) in Computer Science (FinTech and Blockchain)

MQF/EQF Level 7, 90 ECTS

Required modules: Object Oriented Modelling (8 ECTS), Research Methods in Computer Science (6 ECTS), Fintech and Blockchain (12 ECTS), Final Project (20 ECTS) or Practicum (20 ECTS), plus elective modules totalling 12–32 ECTS.